A new alpha seeding method for support vector machine training

被引：0

作者：

Feng, D ^{[1
]}

Shi, WK ^{[1
]}

Guo, HW ^{[1
]}

Chen, LZ ^{[1
]}

机构：

[1] Shanghai Jiao Tong Univ, Sch Elect & Elect Engn, Shanghai 200030, Peoples R China

来源：

ADVANCES IN NATURAL COMPUTATION, PT 1, PROCEEDINGS | 2005年 / 3610卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In order to get good hyperparameters of SVM, user needs to conduct extensive cross-validation such as leave-one-out (LOO) cross-validation. Alpha seeding is often used to reduce the cost of SVM training. Compared with the existing schemes of alpha seeding, a new efficient alpha seeding method is proposed. Through some examples, its good performance has been proved. Interpretation from both geometrical and mathematical view is also given.

引用

页码：679 / 682

页数：4

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